We introduce a variational quantum algorithm to solve unconstrained black box binary optimization problems, i.e., problems in which the objective function is given as black box. This is in contrast to the typical setting of quantum algorithms for optimization where a classical objective function is provided as a given Quadratic Unconstrained Binary Optimization problem and mapped to a sum of Pauli operators. Furthermore, we provide theoretical justification for our method based on convergence guarantees of quantum imaginary time evolution. To investigate the performance of our algorithm and its potential advantages, we tackle a challenging real-world optimization problem: feature selection. This refers to the problem of selecting a subset of relevant features to use for constructing a predictive model such as fraud detection. Optimal feature selection -- when formulated in terms of a generic loss function -- offers little structure on which to build classical heuristics, thus resulting primarily in 'greedy methods'. This leaves room for (near-term) quantum algorithms to be competitive to classical state-of-the-art approaches. We apply our quantum-optimization-based feature selection algorithm, termed VarQFS, to build a predictive model for a credit risk data set with 20 and 59 input features (qubits) and train the model using quantum hardware and tensor-network-based numerical simulations, respectively. We show that the quantum method produces competitive and in certain aspects even better performance compared to traditional feature selection techniques used in today's industry.
Warm Start of Variational Quantum Algorithms for Quadratic Unconstrained Binary Optimization Problems
Deutsches Elektronen-Synchrotron DESY（德国电子同步加速器）；Computation-Based Science and Technology Research Center, The Cyprus Institute（塞浦路斯研究所计算科学与技术研究中心）；Institut für Physik, Humboldt-Universität zu Berlin（柏林洪堡大学物理研究所）；Institute for Quantum Computing Analytics (PGI-12) Jülich Research Center（于利希研究中心量子计算分析研究所）
Energy Landscape Structure of Small Graph Isomorphism Under Variational Optimization
Calcutta Institute of Engineering and Management, Maulana Abul Kalam Azad University of Technology（加尔各答工程与管理学院，穆拉纳·阿布尔·卡拉姆·阿扎德技术大学）；University of Dhaka（达卡大学）；Institute of Theoretical and Applied Informatics, Polish Academy of Sciences（波兰科学院理论与应用信息研究所）；Silesian University of Technology（西里西亚理工大学）
Symplectic Optimization on Bosonic Gaussian States
Universität Hamburg（汉堡大学）；University of Oxford（牛津大学）；The Hamburg Centre for Ultrafast Imaging（汉堡超快成像中心）；IBM Research Europe, The Hartree Centre STFC Laboratory（IBM欧洲研究院，哈特里STFC实验室）
QUBO.jl: A Julia Ecosystem for Quadratic Unconstrained Binary Optimization
Federal University of Rio de Janeiro（里约热内卢联邦大学）；PSR；Davidson School of Chemical Engineering, Purdue University（普渡大学戴维森化学工程学院）；Pontifical Catholic University of Rio de Janeiro（里约热内卢天主教大学）；Research Institute of Advanced Computer Science, Universities Space Research Association（美国大学航天协会高级计算机科学研究所）；Quantum Artificial Intelligence Laboratory, NASA Ames Research Center（NASA艾姆斯研究中心量子人工智能实验室）
Systematic and Efficient Construction of Quadratic Unconstrained Binary Optimization Forms for High-order and Dense Interactions
Recruit Co.（Recruit公司）；Graduate School of Science and Technology Keio University（Keio大学工学研究院）；Department of Applied Physics and Physico-Informatics Keio University（Keio大学应用物理与物理信息学系）；Keio University（Keio大学）；Keio University Sustainable Quantum Artificial Intelligence Center (KSQAIC)（Keio大学可持续量子人工智能中心）；Human Biology-Microbiome-Quantum Research Center (WPI-Bio2Q)（人类生物学-微生物群-量子研究中心（WPI-Bio2Q））
